Abstract
Constraint Based Periodic Pattern Mining in Multiple Longest Common Subsequences
Highlights
The uniform interval of time to reflect certain behavior of an entity is vital in many applications such as frequently sold products in a retail market, interval pattern in DNA sequences, stock growth, transactions in superstore, gene expression data analysis [20, 8, 3] etc
To find the periodic pattern in multiple longest common subsequences (MLCS), we propose a new and efficient pattern enumeration approach based on the ideas of frequent pattern mining techniques
The algorithm was implemented on the message-passing interface (MPI) system and run on local IBM SP3 machine
Summary
The uniform interval of time to reflect certain behavior of an entity is vital in many applications such as frequently sold products in a retail market, interval pattern in DNA sequences, stock growth, transactions in superstore, gene expression data analysis [20, 8, 3] etc. To find the periodic pattern in MLCS, we propose a new and efficient pattern enumeration approach based on the ideas of frequent pattern mining techniques. The construction of consensus tree detects symbol, sequence, and segment patterns without periodicity, within subsection of the series. All the node of the consensus tree exists based on confidence greater than or equal to the user-specified periodicity threshold. Integrating two techniques and developed an efficient algorithm known as Constraint Based Periodicity Pattern Mining (CBPPM) technique to solve MLCS problem and to find periodic pattern in MLCS. CBPPM algorithm is proposed based on two points; the first we search for all subsequences of any length among given input strings. Implicit user-defined constraint play vital role in pruning the search space of the FP-tree and influence time complexity.
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